Deep Learning‐Based Fault Diagnosis for Asymmetric Cascaded H‐Bridge Multilevel Inverters Under Dynamic Motor Loads

ABSTRACT This paper presents a novel deep learning‐based approach for open‐circuit switch fault detection in cascaded H‐bridge (CHB) multilevel inverters. Unlike most studies that use static loads, we employ a squirrel‐cage induction motor to emulate realistic industrial operating conditions. A three‐phase 9‐level asymmetric CHB inverter prototype is driven by an FPGA‐based controller. High‐resolution current and voltage data are collected across 50 different speed‐torque operating points, comprising 25 classes (1 healthy, 24 faulty). The proposed CNN‐LSTM hybrid model achieves an average accuracy of 97.4% under variable load conditions (with raw signals only, as an ablation baseline) and 99.0% on a held‐out test set (with hybrid features). A hybrid feature selection method (Mutual Information and Random Forest) reduces computational load, enabling real‐time deployment on an embedded platform (Raspberry Pi 5) with 15–20 ms response time. Extensive comparisons, ablation studies, and robustness analyses demonstrate the superiority of our approach over state‐of‐the‐art methods.